Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection
AbstractMulti-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets. View Full-Text
Share & Cite This Article
Lee, J.; Kim, D.-W. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection. Entropy 2016, 18, 405.
Lee J, Kim D-W. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection. Entropy. 2016; 18(11):405.Chicago/Turabian Style
Lee, Jaesung; Kim, Dae-Won. 2016. "Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection." Entropy 18, no. 11: 405.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.